2007/03/01 by Andrew Lumsdaine, Douglas Gregor, Bruce Hendrickson +1 · 7 citations
Computer Science · #Parallel Computing and Optimization Techniques #Graph Theory and Algorithms #Distributed and Parallel Computing Systems #Computer science #Scalability #Graph #Theoretical computer science #Wait-for graph #Parallel computing #Software #Mainstream #Distributed computing #Graph rewriting #Programming language #Database
paper · doi:10.1142/s0129626407002843
openalex publication_date 2007/03/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/03
Graph algorithms are becoming increasingly important for solving many problems in scientific computing, data mining and other domains. As these problems grow in scale, parallel computing resources are required to meet their computational and memory requirements. Unfortunately, the algorithms, software, and hardware that have worked well for developing mainstream parallel scientific applications are not necessarily effective for large-scale graph problems. In this paper we present the inter-relationships between graph problems, software, and parallel hardware in the current state of the art and discuss how those issues present inherent challenges in solving large-scale graph problems. The range of these challenges suggests a research agenda for the development of scalable high-performance software for graph problems.